File cooperative management method and system based on subway construction project

Abstract

The present application relates to the technical field of subway construction, in particular to a file collaborative management method and system based on subway construction engineering, comprising the following steps: S1, file collection: collecting real-time data of subway construction site; S2, classification and marking: automatically classifying uploaded real-time data; S3, multi-party collaborative editing and version management: recording the update of real-time data; S4, dynamic construction modeling and simulation: establishing a dynamic three-dimensional model of construction site; S5, data security and permission control: assigning different access permissions according to user roles; S6, automatic risk assessment and early warning: automatically performing risk assessment; S7, file archiving and sharing: archiving all construction data to generate engineering files. The present application improves the intelligent level of construction management and ensures the smooth progress and quality control of the construction process.

Description

Technical Field

[0001] This invention relates to the field of subway construction technology, and in particular to a collaborative management method and system for archives based on subway construction projects. Background Technology

[0002] Subway construction projects involve a large amount of real-time data, such as construction progress, material usage, and safety monitoring data. Traditional record management methods rely on manual recording and sorting, which makes it difficult to collect and manage this data comprehensively and in a timely manner. This leads to data gaps, inefficient classification and labeling, and affects construction quality and safety. In addition, existing methods are inefficient in processing text, image, and video data, and the accuracy of classification and labeling is difficult to guarantee, affecting the organization and searchability of the data.

[0003] In multi-party collaborative subway construction projects, members of different project teams need to share and edit the same dataset. However, existing methods lack an effective version management mechanism, which can easily lead to data inconsistency and information loss. Data tracing and recovery are difficult, affecting project coordination and management. In addition, existing document management systems can usually only perform static data recording and storage, lacking dynamic modeling and simulation capabilities, and are unable to monitor construction progress and quality in a timely manner, or identify and solve potential problems.

[0004] Traditional document management systems also have shortcomings in data security and access control, which can easily lead to data leakage or unauthorized operations. They lack a multi-level access control system. At the same time, existing systems lack automated risk assessment and early warning mechanisms, cannot automatically identify construction risks based on real-time and historical data, and lack effective early warning notifications and preventive measures, making it difficult to ensure the safety and efficiency of construction management. Against this background, this invention proposes an improved document collaborative management method and system, aiming to solve the shortcomings of existing technologies. Summary of the Invention

[0005] To achieve the above objectives, this invention provides a method and system for collaborative management of archives based on subway construction projects.

[0006] A collaborative management method for archives based on subway construction projects includes the following steps:

[0007] S1, Archive Collection: Collect real-time data from the subway construction site, including project progress, material usage, and safety monitoring data, and upload the collected real-time data to the cloud storage platform;

[0008] S2, Classification and Labeling: Using natural language processing and image recognition technology, the uploaded real-time data is automatically classified and labeled according to a preset labeling system;

[0009] S3, multi-party collaborative editing and version management: On the cloud storage platform, a virtual workspace is created, and project team members can edit, modify and supplement data in real time according to their permissions, and update it synchronously in real time. A version management mechanism is established to record the updates of real-time data and generate version numbers.

[0010] S4, Dynamic Construction Modeling and Simulation: Based on real-time data edited collaboratively by multiple parties, a dynamic three-dimensional model of the construction site is established, and the construction process is simulated. By comparing real-time data with model data, the construction progress and quality are monitored in real time, potential problems are identified, and the construction plan is adjusted in a timely manner.

[0011] S5, Data Security and Access Control: Employs data encryption technology and a multi-level access control system, and assigns different access permissions based on user roles;

[0012] S6, Automated Risk Assessment and Early Warning: Based on real-time and historical data, it automatically conducts risk assessments, identifies construction risks, and generates early warning notifications to prompt relevant personnel to take preventive measures and reduce construction risks;

[0013] S7, Archive Archiving and Sharing: After the project is completed, all construction data is archived to generate project archives, and the data is shared with relevant departments and units to promote information exchange and interconnection.

[0014] Furthermore, the file collection in S1 includes:

[0015] S11, Project Progress Monitoring: Cameras and laser scanners are deployed at the construction site to monitor the construction progress in real time and automatically record and upload the construction progress data;

[0016] S12, Material Usage Record: Using RFID tags and readers, construction materials are marked and tracked. On-site staff use mobile terminal devices to scan RFID tags, record the material usage in real time, and upload the recorded data to the cloud storage platform.

[0017] S13, Safety monitoring data acquisition: Deploy various safety monitoring sensors, including temperature sensors, humidity sensors, pressure sensors, vibration sensors and gas detection sensors, to monitor environmental parameters and safety conditions at the construction site in real time;

[0018] S14, Data Aggregation and Transmission: All collected real-time data is aggregated, preliminarily processed and integrated, and then uploaded to the cloud storage platform.

[0019] Furthermore, the classification and labeling in S2 include:

[0020] S21, Natural Language Processing Classification: The text data in the uploaded real-time data is processed using natural language processing technology, including word segmentation, part-of-speech tagging and semantic analysis, and the text data is automatically classified into project progress, material usage and safety monitoring reports.

[0021] S22, Image Recognition and Classification: The image and video data in the uploaded real-time data are classified by convolutional neural network (CNN) into construction progress images, material inventory images and safety monitoring videos.

[0022] S23, Pre-set Tag System: Based on the subway construction project, a pre-set tag system is established, including project name, date, location, data type, and responsible person. The tag system will be used to label the categorized data.

[0023] S24, Automatic Tagging: Based on the tagging system, the classified data is automatically tagged. Text data will be automatically tagged according to the content, including construction progress, material consumption, and safety alarms. Image and video data will be automatically tagged according to the content, including on-site photos, inventory images, and safety monitoring videos.

[0024] S25, Data Storage and Indexing: Store the classified and tagged real-time data in a cloud storage platform and generate an index.

[0025] Furthermore, the multi-party collaborative editing and version management in S3 includes:

[0026] S31, Virtual Workspace Creation: On the cloud storage platform, virtual workspaces are created. Each project team is allocated an independent workspace according to its tasks and responsibilities. Each project team member can access and operate the corresponding workspace according to the preset permissions.

[0027] S32, Real-time Data Editing and Synchronization: Each project team member can edit, modify, and supplement real-time data in the virtual workspace, and update each change in real-time data on the interface of all relevant users;

[0028] S33, Access Control: Based on user roles and responsibilities, different levels of access and editing permissions are assigned. Access control includes read-only permissions, editing permissions, and approval permissions.

[0029] S34, Version Management Mechanism: Establish a version management mechanism to record each update of real-time data and generate a unique version number;

[0030] S35, Version History Query and Recovery: Supports users to query historical versions, compare differences between different versions, and perform data rollback and recovery.

[0031] Furthermore, the dynamic construction modeling and simulation in S4 includes:

[0032] S41, Data Integration and Preprocessing: Based on real-time data after multi-party collaborative editing, data integration and preprocessing are performed. Preprocessing includes cleaning, noise reduction, and format conversion.

[0033] S42, Dynamic 3D Model Establishment: Using preprocessed real-time data, a dynamic 3D model of the construction site is established. The dynamic 3D model includes information on construction progress, material usage, and safety monitoring data.

[0034] S43, Construction Process Simulation: Based on the dynamic 3D model, the construction process is simulated. By inputting real-time data and historical data, various procedures and operations in the construction process are simulated to generate a dynamic construction simulation scenario.

[0035] S44, Data Comparison and Monitoring: By comparing real-time data with dynamic 3D model data, construction progress and quality can be monitored in real time;

[0036] S45, Potential Problem Identification and Adjustment: Based on data comparison and monitoring results, potential problems in the construction process are automatically identified, including structural problems and equipment failures, and adjustment suggestions are provided. Managers adjust the construction plan in a timely manner according to the adjustment suggestions.

[0037] Furthermore, the dynamic three-dimensional model in S42 includes:

[0038] S421, Point Cloud Data Processing: The three-dimensional coordinates of each point on the construction site are transformed. Using an iterative nearest-point algorithm, the real-time point cloud data is registered with the dynamic three-dimensional model point cloud data. The calculation formula is as follows:

[0039]

[0040]

[0041] Where R is the rotation matrix, T is the translation vector, and p i and q i These are points in real-time point cloud data and dynamic 3D model point cloud data, respectively.

[0042] S422, Surface Reconstruction: The point cloud surface is reconstructed using a triangular mesh algorithm to generate a 3D model of the construction site. The calculation formula is as follows:

[0043]

[0044] Where V is the reconstructed surface volume, v i and v i+1 These are the vertices of adjacent triangles;

[0045] S423, Construction Progress Integration: Maps construction progress data onto a 3D model of the construction site to display the current construction status. The calculation formula is:

[0046]

[0047] Where P(t) is the construction progress position at time t, P0 is the initial position, and v(t′) is the construction speed at time t′;

[0048] S424, Material Usage Integration: Maps material usage data to a 3D model of the construction site, displaying material consumption and inventory status. The calculation formula is as follows:

[0049]

[0050] Where M(t) is the material inventory at time t, M0 is the initial inventory, and m i (t) represents the amount of material i consumed at time t;

[0051] S425, Safety Monitoring Data Integration: Mapping safety monitoring data onto a 3D model of the construction site to display the current safety status. The calculation formula is as follows:

[0052] S(t) = {s1(t), s2(t), ..., s k (t)};

[0053] Where S(t) is the safety monitoring dataset at time t, s i (t) is the reading of the i-th safety monitoring sensor at time t.

[0054] Furthermore, the construction process simulation in S43 includes:

[0055] S431, Data Input and Integration: Input real-time and historical data from the construction site into the simulation platform to form a dataset D for simulation. sim ;

[0056] S432, Construction Process Modeling: Using the process modeling function M process Based on the input dataset and process parameters (such as time, resource requirements, and inter-process dependencies), a construction process model is established, and the calculation formula is as follows:

[0057]

[0058] Where, α i and β i These are weighting coefficients. It is the i-th data input, P iIt is the parameter of the i-th process;

[0059] S433, Dynamic Simulation Calculation: Start the simulation engine, based on the construction process model M process and data input D sim Dynamic simulation calculations are performed, updating the construction site status in real time during the simulation process, and generating construction progress and resource usage at various time points. The calculation formula is as follows:

[0060]

[0061] Where S(t) represents the construction site state at time t, and D sim (t′) is the simulation data input at time t′;

[0062] S434, Dynamic Construction Scenario Generation: Based on the simulation results, a dynamic construction scenario is generated that includes changes in the 3D model and updates to the construction progress. The calculation formula is as follows:

[0063]

[0064] in, It is a scene generation function that generates the construction scene at time t.

[0065] Furthermore, the identification and adjustment of potential problems in S45 includes:

[0066] S451, Data Comparison and Monitoring Result Collection: Based on dynamic simulation calculations and actual construction data, compare the differences between the two, calculate the error, and generate a monitoring report of the error analysis results. Record any anomalies and deviations during the construction process. The calculation formula is as follows:

[0067] ∈ i =||Rp i +Tq i ||;

[0068] Where, ∈ i It is the error value at the i-th point;

[0069] S452, Potential Problem Identification: Utilizing error analysis results, structural problems are identified, and based on real-time monitoring data, operational equipment faults are identified. The calculation formula is as follows:

[0070] P issue (t)=δ(∈ i (t)>∈ threshold );

[0071] Among them, P issue (t) represents the probability of a potential problem at time t, and δ is an indicator function that determines the probability of an error exceeding a threshold ∈ [0, t]. threshold This indicates the presence of potential problems;

[0072] S453, Adjustment Recommendation Generation: Based on identified potential problems, adjustment recommendations are generated, including adjustments to construction procedures and resource reallocation. Optimization algorithms are used to ensure the effectiveness and feasibility of these recommendations. The calculation formula is as follows:

[0073]

[0074] Where A is the adjustment suggestion, γ is the optimization function, and S i It's about adjusting the strategy;

[0075] S454, Management Decision Support: Based on the adjustment suggestions, managers promptly adjust the construction plan, including revising the construction schedule and replacing or repairing equipment, and record the implementation effect of the adjustment plan for continuous monitoring.

[0076] Furthermore, the automated risk assessment and early warning in S6 includes:

[0077] S61, Data Collection: Collect real-time data and integrate historical data to form a dataset D for risk assessment. risk ;

[0078] S62, Risk assessment model establishment: Preprocess the collected risk assessment dataset, identify key factors affecting construction risks, including geological anomalies, equipment failures, and weather changes, and establish a risk assessment model;

[0079] S63, Risk Identification and Quantification: Based on the risk assessment model, calculate the current risk value using real-time and historical data, and then quantify and assess the risk value to determine the risk level (e.g., low, medium, high).

[0080] S64, Warning Notification Generation: Set warning rules according to risk level. When the risk value exceeds the preset threshold, trigger the warning and generate a warning notification.

[0081] S65, Implementation of Preventive Measures: Based on the risk type and level, recommend appropriate preventive measures, including strengthening monitoring, adjusting construction plans, and repairing equipment. Relevant personnel shall implement the preventive measures and record the implementation status and feedback results.

[0082] The document collaborative management system based on subway construction projects is used to implement the above-mentioned document collaborative management method based on subway construction projects, and includes the following modules:

[0083] Data acquisition module: Collects real-time data from the subway construction site, including project progress, material usage, and safety monitoring data, and uploads the collected real-time data to the cloud storage platform;

[0084] Data classification and labeling module: Utilizes natural language processing and image recognition technologies to automatically classify uploaded real-time data and label it according to a preset labeling system;

[0085] Multi-party collaborative editing and version management module: On the cloud storage platform, a virtual workspace is created, and project team members can edit, modify and supplement data in real time according to their permissions, and update it synchronously in real time. A version management mechanism is established to record the updates of real-time data and generate version numbers.

[0086] Dynamic construction modeling and simulation module: Based on real-time data edited collaboratively by multiple parties, a dynamic 3D model of the construction site is established, and the construction process is simulated. By comparing real-time data with model data, the construction progress and quality are monitored in real time, potential problems are identified, and the construction plan is adjusted in a timely manner.

[0087] Data security and access control module: Employs data encryption technology and a multi-level access control system, and assigns different access permissions based on user roles;

[0088] Automated risk assessment and early warning module: Based on real-time and historical data, it automatically conducts risk assessments, identifies construction risks, and generates early warning notifications to prompt relevant personnel to take preventive measures;

[0089] Archive and Sharing Module: After the project is completed, all construction data is archived to generate project archives, and the data is shared with relevant departments and units.

[0090] The beneficial effects of this invention are:

[0091] This invention achieves comprehensive, accurate, and real-time collection and management of subway construction site data through archive collection, classification and labeling, multi-party collaborative editing and version management. Various types of data, including construction progress, material usage and safety monitoring data, are automatically classified and labeled to ensure data organization and easy searchability. The multi-party collaborative editing and version management mechanism ensures real-time synchronous updates and historical traceability of data, improving the efficiency and accuracy of archive management.

[0092] This invention, through dynamic construction modeling and simulation, establishes a dynamic three-dimensional model of the construction site based on real-time data edited collaboratively by multiple parties, and simulates the construction process. By comparing model data with actual data in real time, it can monitor construction progress and quality in a timely manner, identify potential problems and provide adjustment suggestions, and ensure the optimization and timely adjustment of construction plans. Through comprehensive simulation and monitoring of the construction process, it improves the level of intelligent construction management and ensures the smooth progress and quality control of the construction process.

[0093] This invention, through automated risk assessment and early warning, automatically conducts risk assessment based on real-time and historical data, identifies potential risks during construction, such as geological anomalies and equipment failures, and generates early warning notifications to prompt relevant personnel to take timely preventive measures to reduce construction risks. The preventive measure suggestions and execution feedback mechanism provided by the system further improve the safety and efficiency of construction management, ensuring the safety and reliability of the construction process. Attached Figure Description

[0094] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0095] Figure 1 This is a schematic diagram of the management method flow according to an embodiment of the present invention;

[0096] Figure 2 This is a schematic diagram of the system functional modules according to an embodiment of the present invention. Detailed Implementation

[0097] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0098] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0099] like Figure 1 As shown, the collaborative management method for archives based on subway construction projects includes the following steps:

[0100] S1, Archive Collection: Collect real-time data from the subway construction site, including project progress, material usage, and safety monitoring data, and upload the collected real-time data to the cloud storage platform;

[0101] S2, Classification and Labeling: Utilizing natural language processing and image recognition technologies, the uploaded real-time data is automatically classified and labeled according to a preset labeling system to ensure the data's organization and ease of searching;

[0102] S3, multi-party collaborative editing and version management: On the cloud storage platform, a virtual workspace is created, and project team members can edit, modify and supplement data in real time according to their permissions, and update it synchronously in real time. A version management mechanism is established to record the updates of real-time data and generate version numbers. Users can query and restore any historical version as needed to ensure data traceability.

[0103] S4, Dynamic Construction Modeling and Simulation: Based on real-time data edited collaboratively by multiple parties, a dynamic three-dimensional model of the construction site is established, and the construction process is simulated. By comparing real-time data with model data, the construction progress and quality are monitored in real time, potential problems are identified, and the construction plan is adjusted in a timely manner.

[0104] S5, Data Security and Access Control: Employs data encryption technology and a multi-level access control system to ensure the security of archive data during transmission and storage, and assigns different access permissions according to user roles to prevent unauthorized operations;

[0105] S6, Automated Risk Assessment and Early Warning: Based on real-time and historical data, it automatically conducts risk assessments, identifies construction risks, and generates early warning notifications to prompt relevant personnel to take preventive measures and reduce construction risks;

[0106] S7, Archive Archiving and Sharing: After the project is completed, all construction data is archived to generate project archives, and the data is shared with relevant departments and units to promote information exchange and interconnection;

[0107] Through the above steps, efficient and accurate management of subway construction project archives was achieved, the level of intelligent archive management was improved, the dynamic modeling and simulation capabilities of data were guaranteed, real-time monitoring and adjustment during the construction process were ensured, and construction quality and management efficiency were further improved.

[0108] The collection of files in S1 includes:

[0109] S11, Project Progress Monitoring: Cameras and laser scanners are deployed at the construction site to monitor the construction progress in real time and automatically record and upload the construction progress data;

[0110] S12, Material Usage Record: RFID tags and readers are used to mark and track construction materials. On-site staff use mobile terminal devices to scan RFID tags to record the material usage in real time and upload the recorded data to the cloud storage platform to ensure the accuracy and timeliness of material usage data.

[0111] S13, Safety monitoring data acquisition: Deploy various safety monitoring sensors, including temperature sensors, humidity sensors, pressure sensors, vibration sensors and gas detection sensors, to monitor environmental parameters and safety conditions at the construction site in real time;

[0112] S14, Data Aggregation and Transmission: All collected real-time data is aggregated, preliminarily processed and integrated, and then uploaded to the cloud storage platform to ensure data security during transmission.

[0113] The above steps ensured the comprehensiveness, accuracy, and timeliness of data on the subway construction site's progress, material usage, and safety monitoring, providing a reliable data foundation for subsequent data classification, labeling, and analysis.

[0114] The classifications and labels in S2 include:

[0115] S21, Natural Language Processing Classification: The text data in the uploaded real-time data is processed using natural language processing technology, including word segmentation, part-of-speech tagging and semantic analysis, and the text data is automatically classified into project progress, material usage and safety monitoring reports.

[0116] Natural language processing is specifically categorized into:

[0117] S211, Word segmentation algorithm: This algorithm segments text data into words, and the calculation formula is as follows:

[0118]

[0119] Among them, P(w i |c) indicates that word w is in category c. i The probability, f(w) i c) represents word w in category c. i frequency, N c The total number of words in category c;

[0120] S212, Part-of-Speech Tagging: Part-of-speech tagging is performed using a Hidden Markov Model (HMM), and the calculation formula is as follows:

[0121]

[0122] Where T represents the part-of-speech sequence, W represents the word sequence, and P(t) i |ti-1 P(w) represents the part-of-speech transition probability. i |t i ) indicates the part of speech t i The word w is generated below i The probability of;

[0123] S213, Text Classification: Text classification is performed using a Bayesian classifier. The calculation formula is as follows:

[0124]

[0125] Where P(c|d) is the probability that document d belongs to category c, P(c) is the prior probability of category c, and P(w) is the probability of document d belonging to category c. i |c) represents the word w in category c. i The conditional probability, P(d) is the probability of document d;

[0126] S22, Image Recognition and Classification: The image and video data in the uploaded real-time data are classified by convolutional neural network (CNN) into construction progress images, material inventory images and safety monitoring videos.

[0127] Convolutional Neural Networks (CNNs) include:

[0128] Convolutional layer: The convolutional layer is used to extract local features of the input image. It calculates the feature map through convolution operations. The calculation formula is as follows:

[0129]

[0130] Among them, Z i,j,k X represents the value of the output feature map at position (i,j) and channel k. i+m,j+n W represents the value of the input image at position (i+m, j+n). m,n,k b represents the weights of the convolution kernel at position (m,n) and channel k. k Indicates bias;

[0131] Pooling layer: The pooling layer is used to downsample the feature map. It uses the max pooling algorithm, and the calculation formula is as follows:

[0132] P i,j,k =max 0≤m<M,0≤n<N (Z i·M+m,j·N+n,k );

[0133] Among them, P i,j,k Z represents the value of the pooling layer output at position (i,j) and channel k. i·M+m,j·N+n,k This represents the value of the input feature map within the pooling window;

[0134] Fully connected layer: The fully connected layer flattens the pooled feature map into a one-dimensional vector, which is then input into the fully connected layer for classification. The calculation formula is as follows:

[0135]

[0136] Among them, y i w represents the value of the i-th neuron in the output of the fully connected layer. ij Indicates the connection weight, x j b represents the j-th element of the input feature vector. i Indicates bias;

[0137] S23, Pre-set Tag System: Based on the subway construction project, a pre-set tag system is established, including project name, date, location, data type, and responsible person. The tag system will be used to label the categorized data.

[0138] S24, Automatic Tagging: Based on the tagging system, the classified data is automatically tagged. Text data will be automatically tagged according to the content, including construction progress, material consumption, and safety alarms. Image and video data will be automatically tagged according to the content, including on-site photos, inventory images, and safety monitoring videos.

[0139] S25, Data storage and indexing: The classified and tagged real-time data is stored in the cloud storage platform and an index is generated to facilitate fast retrieval and searching. The index will be based on a preset tag system to ensure the data's organization and ease of searching.

[0140] Through the above steps, it is ensured that the uploaded real-time data can be automatically classified using natural language processing and image recognition technologies, and marked according to a preset labeling system, thereby achieving data organization and easy searchability.

[0141] Multi-party collaborative editing and version control in S3 include:

[0142] S31, Virtual Workspace Creation: On the cloud storage platform, virtual workspaces are created. Each project team is allocated an independent workspace according to its tasks and responsibilities. Each project team member can access and operate the corresponding workspace according to the preset permissions.

[0143] S32, Real-time Data Editing and Synchronization: Each project team member can edit, modify, and supplement real-time data in the virtual workspace, and update each change in real-time data on the interfaces of all relevant users to avoid data inconsistency issues;

[0144] S33, Access Control: Based on user roles and responsibilities, different levels of access and editing permissions are assigned to ensure data security and standardized management. Access control includes read-only permissions, editing permissions, and auditing permissions to prevent unauthorized operations.

[0145] S34, Version Management Mechanism: Establish a version management mechanism to record each update of real-time data and generate a unique version number. The version management mechanism can record detailed information about each change, including modification time, modifier, and modified content.

[0146] S35, Version History Query and Recovery: Supports users to query historical versions, compare the differences between different versions, and perform data rollback and recovery to ensure that the correct version can be quickly restored in the event of accidental operation or data error.

[0147] The above steps ensure the real-time nature and consistency of collaborative editing among multiple parties. At the same time, the version management mechanism records and tracks each data update in detail, ensuring data traceability and security.

[0148] Dynamic construction modeling and simulation in S4 includes:

[0149] S41, Data Integration and Preprocessing: Based on real-time data after multi-party collaborative editing, data integration and preprocessing are performed. Preprocessing includes cleaning, noise reduction, and format conversion to ensure data consistency and integrity.

[0150] S42, Dynamic 3D Model Establishment: Using pre-processed real-time data, a dynamic 3D model of the construction site is established. The dynamic 3D model includes information on construction progress, material usage, and safety monitoring data to ensure the accuracy and practicality of the model.

[0151] S43, Construction Process Simulation: Based on the dynamic 3D model, the construction process is simulated. By inputting real-time data and historical data, various procedures and operations in the construction process are simulated to generate a dynamic construction simulation scenario.

[0152] S44, Data Comparison and Monitoring: By comparing real-time data with dynamic 3D model data, construction progress and quality can be monitored in real time;

[0153] S45, Potential Problem Identification and Adjustment: Based on data comparison and monitoring results, potential problems in the construction process are automatically identified, including structural problems and equipment failures, and adjustment suggestions are provided. Managers adjust the construction plan in a timely manner according to the adjustment suggestions to ensure the smooth progress of construction.

[0154] Through the above steps, we can ensure that dynamic construction modeling and simulation can establish an accurate dynamic three-dimensional model of the construction site based on real-time data edited by multiple parties, simulate the construction process, monitor the construction progress and quality in real time, identify and solve potential problems, and optimize the construction plan.

[0155] The dynamic 3D model in S42 includes:

[0156] S421, Point Cloud Data Processing: The three-dimensional coordinates of each point on the construction site are transformed to ensure the accuracy of the point cloud data. The real-time point cloud data is registered with the dynamic 3D model point cloud data using an iterative nearest-point algorithm. The calculation formula is as follows:

[0157]

[0158]

[0159] Where R is the rotation matrix, T is the translation vector, and p i and q i These are points in real-time point cloud data and dynamic 3D model point cloud data, respectively.

[0160] S422, Surface Reconstruction: The point cloud surface is reconstructed using a triangular mesh algorithm to generate a 3D model of the construction site. The calculation formula is as follows:

[0161]

[0162] Where V is the reconstructed surface volume, v i and v i+1 These are the vertices of adjacent triangles;

[0163] S423, Construction Progress Integration: Maps construction progress data onto a 3D model of the construction site to display the current construction status. The calculation formula is:

[0164]

[0165] Where P(t) is the construction progress position at time t, P0 is the initial position, and v(t′) is the construction speed at time t′;

[0166] S424, Material Usage Integration: Maps material usage data to a 3D model of the construction site, displaying material consumption and inventory status. The calculation formula is as follows:

[0167]

[0168] Where M(t) is the material inventory at time t, M0 is the initial inventory, and m i (t) represents the amount of material i consumed at time t;

[0169] S425, Safety Monitoring Data Integration: Mapping safety monitoring data onto a 3D model of the construction site to display the current safety status. The calculation formula is as follows:

[0170] S(t) = {s1(t), s2(t), ..., s k (t)};

[0171] Where S(t) is the safety monitoring dataset at time t, s i (t) is the reading of the i-th safety monitoring sensor at time t;

[0172] Through the above steps, we can ensure that the dynamic 3D model can accurately and comprehensively reflect the current status of the construction site, and integrate construction progress, material usage and safety monitoring data to provide strong support for construction management.

[0173] The construction process simulation in S43 includes:

[0174] S431, Data Input and Integration: Input real-time and historical data from the construction site into the simulation platform to form a dataset D for simulation. sim ;

[0175] S432, Construction Process Modeling: Using the process modeling function M process Based on the input dataset and process parameters (such as time, resource requirements, and inter-process dependencies), a construction process model is established, and the calculation formula is as follows:

[0176]

[0177] Where, α i and β i These are weighting coefficients, which determine the influence of different data and parameters on the model. It is the i-th data input, P i It is the parameter of the i-th process;

[0178] S433, Dynamic Simulation Calculation: Start the simulation engine, based on the construction process model M process and data input D sim Dynamic simulation calculations are performed, updating the construction site status in real time during the simulation process, and generating construction progress and resource usage at various time points. The calculation formula is as follows:

[0179]

[0180] Where S(t) represents the construction site state at time t, and D sim (t′) is the simulation data input at time t′;

[0181] S434, Dynamic Construction Scenario Generation: Based on the simulation results, a dynamic construction scenario is generated that includes changes in the 3D model and updates to the construction progress. The calculation formula is as follows:

[0182]

[0183] in, It is a scene generation function that generates the construction scene at time t;

[0184] Scene generation function Represented as:

[0185] Model(t)=UpdateModel(S(t));

[0186] V(t) = Render(Model(t));

[0187] Through the above steps, this invention can simulate the construction process based on a dynamic three-dimensional model. The simulation platform simulates various procedures and operations during the construction process by inputting real-time and historical data, generating dynamic construction simulation scenarios to help managers optimize construction plans and improve construction efficiency.

[0188] The identification and adjustment of potential problems in S45 include:

[0189] S451, Data Comparison and Monitoring Result Collection: Based on dynamic simulation calculations and actual construction data, compare the differences between the two, calculate the error, and generate a monitoring report of the error analysis results. Record any anomalies and deviations during the construction process. The calculation formula is as follows:

[0190] ∈ i =||Rp i +Tq i ||;

[0191] Where, ∈ i It is the error value at the i-th point;

[0192] S452, Potential Problem Identification: Utilizing error analysis results, structural problems such as deformation and misalignment are identified. Based on real-time monitoring data, operational equipment faults such as mechanical and electrical faults are also identified. The calculation formula is as follows:

[0193] P issue (t)=δ(∈ i (t)>∈ threshold );

[0194] Among them, P issue (t) represents the probability of a potential problem at time t, and δ is an indicator function that determines the probability of an error exceeding a threshold ∈ [0, t]. threshold This indicates the presence of potential problems;

[0195] S453, Adjustment Recommendation Generation: Based on identified potential problems, adjustment recommendations are generated, including adjustments to construction procedures and resource reallocation. Optimization algorithms are used to ensure the effectiveness and feasibility of these recommendations. The calculation formula is as follows:

[0196]

[0197] Where A is the adjustment suggestion, γ is the optimization function, and S i It's about adjusting the strategy;

[0198] The formula for calculating the optimization function γ is:

[0199]

[0200] Where A is the adjustment suggestion, C(A) is the cost function of the adjustment plan, representing the implementation cost of the adjustment suggestion, Q(A) is the construction quality loss function, representing the impact of the adjustment suggestion on construction quality, T(A) is the construction schedule delay function, representing the impact of the adjustment suggestion on construction schedule, and w1, w2, w3 are weighting coefficients, representing the importance weights of cost, quality, and schedule.

[0201] Cost function C(A): The economic cost of implementing the proposed adjustments, including material, equipment, and labor costs, calculated using the following formula:

[0202]

[0203] Among them, c j It is the unit cost of the j-th adjustment action, a j is the number or size of the j-th adjustment action, and M is the total number of adjustment actions;

[0204] Quality loss function Q(A): The negative impact of the proposed adjustments on construction quality, including structural integrity and functionality. The calculation formula is as follows:

[0205]

[0206] Where, q k It is the negative impact coefficient of the k-th adjustment action on quality, a k It represents the number or size of the k-th adjustment action, where K is the total number of adjustment actions;

[0207] Schedule delay function T(A): The impact of the proposed adjustments on the construction schedule, including time extensions and schedule lags, is calculated using the following formula:

[0208]

[0209] Among them, t l It is the delay factor of the l-th adjustment action on the schedule, a l It is the number or scale of the l-th adjustment action, and L is the total number of adjustment actions;

[0210] S454, Management Decision Support: Based on adjustment suggestions, managers promptly adjust construction plans, including revising construction schedules and replacing or repairing equipment, and record the implementation effects of the adjustments for continuous monitoring to ensure construction progress and quality;

[0211] Through the above steps, this invention can automatically identify potential problems in the construction process, including structural problems and equipment failures, based on data comparison and monitoring results, and provide adjustment suggestions. Based on the adjustment suggestions, managers can adjust the construction plan in a timely manner, thereby improving the efficiency and quality of construction management.

[0212] Automated risk assessment and early warning in S6 include:

[0213] S61, Data Collection: Collect real-time data and integrate historical data to form a dataset D for risk assessment. risk ;

[0214] S62, Risk assessment model establishment: Preprocess the collected risk assessment dataset, identify key factors affecting construction risks, including geological anomalies, equipment failures, and weather changes, and establish a risk assessment model;

[0215] The risk assessment model uses a linear regression model, which maps the risk factor vector to the risk value through a weighted summation. The calculation formula is as follows:

[0216]

[0217] Where θ0 is the bias term, θ i X is the weight of the i-th risk factor. i (t) is the value of the i-th risk factor at time t;

[0218] S63, Risk Identification and Quantification: Based on the risk assessment model, calculate the current risk value using real-time and historical data, and then quantify and assess the risk value to determine the risk level (e.g., low, medium, high).

[0219] Quantitative assessment includes:

[0220] Standardized risk value: The standardized risk assessment result R(t), assuming the range of the risk assessment result R(t) is within [R... min ,R max Between ], the calculation formula is:

[0221]

[0222] Among them, R normalized (t) is the standardized risk value, R0 min and R max These are the minimum and maximum values ​​of the risk assessment results, respectively.

[0223] Quantitative risk value: The standardized risk value R normalized (t) is transformed into a specific risk level, which is then converted into a discrete risk level using the following formula (0 represents low risk, 1 represents medium risk, and 2 represents high risk). The calculation formula is as follows:

[0224]

[0225] Where g(R(t)) is the quantified risk value or risk level, and τ1 and τ2 are the thresholds of the risk level. τ1 and τ2 are set according to the specific project situation, such as 0.3 and 0.7.

[0226] S64, Warning Notification Generation: Set warning rules according to risk level. When the risk value exceeds the preset threshold, trigger the warning and generate a warning notification.

[0227] S65, Implementation of Preventive Measures: Based on the risk type and level, recommend corresponding preventive measures, including strengthening monitoring, adjusting construction plans, and repairing equipment. Relevant personnel shall implement the preventive measures and record the implementation status and feedback results.

[0228] Through the above steps, this invention can automatically conduct risk assessments based on real-time and historical data, identify construction risks, generate early warning notifications, and prompt relevant personnel to take preventive measures to reduce construction risks and improve the safety and efficiency of construction management.

[0229] like Figure 2 As shown, the document collaborative management system based on subway construction projects is used to implement the above-mentioned document collaborative management method based on subway construction projects, and includes the following modules:

[0230] Data acquisition module: Collects real-time data from the subway construction site, including project progress, material usage, and safety monitoring data, and uploads the collected real-time data to the cloud storage platform;

[0231] Data classification and labeling module: Utilizes natural language processing and image recognition technologies to automatically classify uploaded real-time data and label it according to a preset labeling system;

[0232] Multi-party collaborative editing and version management module: On the cloud storage platform, a virtual workspace is created, and project team members can edit, modify and supplement data in real time according to their permissions, and update it synchronously in real time. A version management mechanism is established to record the updates of real-time data and generate version numbers.

[0233] Dynamic construction modeling and simulation module: Based on real-time data edited collaboratively by multiple parties, a dynamic 3D model of the construction site is established, and the construction process is simulated. By comparing real-time data with model data, the construction progress and quality are monitored in real time, potential problems are identified, and the construction plan is adjusted in a timely manner.

[0234] Data security and access control module: Employs data encryption technology and a multi-level access control system, and assigns different access permissions based on user roles;

[0235] Automated risk assessment and early warning module: Based on real-time and historical data, it automatically conducts risk assessments, identifies construction risks, and generates early warning notifications to prompt relevant personnel to take preventive measures;

[0236] Archive and Sharing Module: After the project is completed, all construction data is archived to generate project archives, and the data is shared with relevant departments and units.

[0237] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.

[0238] This invention is intended to cover all such substitutions, modifications, and variations falling within the broad scope of the claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A collaborative management method for archives based on subway construction projects, characterized in that: Includes the following steps: S1, Archive Collection: Collect real-time data from the subway construction site, including project progress, material usage, and safety monitoring data, and upload the collected real-time data to the cloud storage platform; S2, Classification and Labeling: Using natural language processing and image recognition technology, the uploaded real-time data is automatically classified and labeled according to a preset labeling system; S3, multi-party collaborative editing and version management: On the cloud storage platform, a virtual workspace is created, and project team members can edit, modify and supplement data in real time according to their permissions, and update it synchronously in real time. A version management mechanism is established to record the updates of real-time data and generate version numbers. S4, Dynamic Construction Modeling and Simulation: Based on real-time data edited collaboratively by multiple parties, a dynamic three-dimensional model of the construction site is established, and the construction process is simulated. By comparing real-time data with model data, the construction progress and quality are monitored in real time, potential problems are identified, and the construction plan is adjusted in a timely manner. S5, Data Security and Access Control: Employs data encryption technology and a multi-level access control system, and assigns different access permissions based on user roles; S6, Automated Risk Assessment and Early Warning: Based on real-time and historical data, it automatically conducts risk assessments, identifies construction risks, and generates early warning notifications to prompt relevant personnel to take preventive measures and reduce construction risks; S7, Archive Archiving and Sharing: After the project is completed, all construction data is archived to generate project archives, and the data is shared with relevant departments and units to promote information exchange and interconnection; The dynamic construction modeling and simulation in S4 includes: S41, Data Integration and Preprocessing: Based on real-time data after multi-party collaborative editing, data integration and preprocessing are performed. Preprocessing includes cleaning, noise reduction, and format conversion. S42, Dynamic 3D Model Establishment: Using preprocessed real-time data, a dynamic 3D model of the construction site is established. The dynamic 3D model includes information on construction progress, material usage, and safety monitoring data. S43, Construction Process Simulation: Based on the dynamic 3D model, the construction process is simulated. By inputting real-time data and historical data, various procedures and operations in the construction process are simulated to generate a dynamic construction simulation scenario. S44, Data Comparison and Monitoring: By comparing real-time data with dynamic 3D model data, construction progress and quality can be monitored in real time; S45, Potential Problem Identification and Adjustment: Based on data comparison and monitoring results, potential problems in the construction process are automatically identified, including structural problems and equipment failures, and adjustment suggestions are provided. Managers adjust the construction plan in a timely manner according to the adjustment suggestions. The construction process simulation in S43 includes: S431, Data Input and Integration: Input real-time and historical data from the construction site into the simulation platform to form a dataset for simulation. ; S432, Construction Process Modeling: Using Process Modeling Functions Based on the input dataset and process parameters, a construction process model is established, and the calculation formula is as follows: ; in, and These are weighting coefficients. It is the first One data input, It is the first Each process parameter; S433, Dynamic Simulation Calculation: Start the simulation engine based on the construction process model. and data input Dynamic simulation calculations are performed, updating the construction site status in real time during the simulation process, and generating construction progress and resource usage at various time points. The calculation formula is as follows: ; in, Indicates time The construction site conditions at that time It is time Simulation data input at the time; S434, Dynamic Construction Scenario Generation: Based on the simulation results, a dynamic construction scenario is generated that includes changes in the 3D model and updates to the construction progress. The calculation formula is as follows: ; in, It is a scene generation function, and the generation time is... Construction scene at the time.

2. The method for collaborative management of archives based on subway construction projects according to claim 1, characterized in that, The file collection in S1 includes: S11, Project Progress Monitoring: Cameras and laser scanners are deployed at the construction site to monitor the construction progress in real time and automatically record and upload the construction progress data; S12, Material Usage Record: Using RFID tags and readers, construction materials are marked and tracked. On-site staff use mobile terminal devices to scan RFID tags, record the material usage in real time, and upload the recorded data to the cloud storage platform. S13, Safety monitoring data acquisition: Deploy various safety monitoring sensors, including temperature sensors, humidity sensors, pressure sensors, vibration sensors and gas detection sensors, to monitor environmental parameters and safety conditions at the construction site in real time; S14, Data Aggregation and Transmission: All collected real-time data is aggregated, preliminarily processed and integrated, and then uploaded to the cloud storage platform.

3. The method for collaborative management of archives based on subway construction projects according to claim 1, characterized in that, The classification and labeling in S2 include: S21, Natural Language Processing Classification: The text data in the uploaded real-time data is processed using natural language processing technology, including word segmentation, part-of-speech tagging and semantic analysis, and the text data is automatically classified into project progress, material usage and safety monitoring reports. S22, Image Recognition and Classification: The image and video data in the uploaded real-time data are classified by a convolutional neural network into construction progress images, material inventory images and safety monitoring videos. S23, Pre-set Tag System: Based on the subway construction project, a pre-set tag system is established, including project name, date, location, data type, and responsible person. The tag system will be used to label the categorized data. S24, Automatic Tagging: Based on the tagging system, the classified data is automatically tagged. Text data will be automatically tagged according to the content, including construction progress, material consumption, and safety alarms. Image and video data will be automatically tagged according to the content, including on-site photos, inventory images, and safety monitoring videos. S25, Data Storage and Indexing: Store the classified and tagged real-time data in a cloud storage platform and generate an index.

4. The method for collaborative management of archives based on subway construction projects according to claim 1, characterized in that, The multi-party collaborative editing and version management in S3 includes: S31, Virtual Workspace Creation: On the cloud storage platform, virtual workspaces are created. Each project team is allocated an independent workspace according to its tasks and responsibilities. Each project team member can access and operate the corresponding workspace according to the preset permissions. S32, Real-time Data Editing and Synchronization: Each project team member can edit, modify, and supplement real-time data in the virtual workspace, and update each change in real-time data on the interface of all relevant users; S33, Access Control: Based on user roles and responsibilities, different levels of access and editing permissions are assigned. Access control includes read-only permissions, editing permissions, and approval permissions. S34, Version Management Mechanism: Establish a version management mechanism to record each update of real-time data and generate a unique version number; S35, Version History Query and Recovery: Supports users to query historical versions, compare differences between different versions, and perform data rollback and recovery.

5. The method for collaborative management of archives based on subway construction projects according to claim 1, characterized in that, The dynamic three-dimensional model in S42 includes: S421, Point Cloud Data Processing: The three-dimensional coordinates of each point on the construction site are transformed. Using an iterative nearest-point algorithm, the real-time point cloud data is registered with the dynamic three-dimensional model point cloud data. The calculation formula is as follows: ; ; in, It is a rotation matrix. It is a translation vector. and These are points in real-time point cloud data and dynamic 3D model point cloud data, respectively. S422, Surface Reconstruction: The point cloud surface is reconstructed using a triangular mesh algorithm to generate a 3D model of the construction site. The calculation formula is as follows: ; in, It is the reconstructed surface volume. and These are the vertices of adjacent triangles; S423, Construction Progress Integration: Maps construction progress data onto a 3D model of the construction site to display the current construction status. The calculation formula is: ; in, It is time At that time, the construction progress position This is the initial position. It is time Construction speed at that time; S424, Material Usage Integration: Maps material usage data to a 3D model of the construction site, displaying material consumption and inventory status. The calculation formula is as follows: ; in, It is time Material inventory at the time, It is the initial inventory. It is the first Such materials in time Consumption amount; S425, Safety Monitoring Data Integration: Mapping safety monitoring data onto a 3D model of the construction site to display the current safety status. The calculation formula is as follows: ; in, Real-time security monitoring dataset, It is the first A safety monitoring sensor in time The reading.

6. The method for collaborative management of archives based on subway construction projects according to claim 5, characterized in that, The identification and adjustment of potential problems in S45 includes: S451, Data Comparison and Monitoring Result Collection: Based on dynamic simulation calculations and actual construction data, compare the differences between the two, calculate the error, and generate a monitoring report of the error analysis results. Record any anomalies and deviations during the construction process. The calculation formula is as follows: ; in, It is the first Error value at each point; S452, Potential Problem Identification: Utilizing error analysis results, structural problems are identified, and based on real-time monitoring data, operational equipment faults are identified. The calculation formula is as follows: ; in, Indicates time The probability of potential problems. It is an indicator function; when the error exceeds a threshold... This indicates the presence of potential problems; S453, Adjustment Recommendation Generation: Based on identified potential problems, adjustment recommendations are generated, including adjustments to construction procedures and resource reallocation. Optimization algorithms are used to ensure the effectiveness and feasibility of these recommendations. The calculation formula is as follows: ; in, It is a suggestion for adjustment. It is an optimization function. It's about adjusting the strategy; S454, Management Decision Support: Based on the adjustment suggestions, managers promptly adjust the construction plan, including revising the construction schedule and replacing or repairing equipment, and record the implementation effect of the adjustment plan for continuous monitoring.

7. The method for collaborative management of archives based on subway construction projects according to claim 1, characterized in that, The automated risk assessment and early warning in S6 includes: S61, Data Collection: Collect real-time data and integrate historical data to form a dataset for risk assessment. ; S62, Risk assessment model establishment: Preprocess the collected risk assessment dataset, identify key factors affecting construction risks, including geological anomalies, equipment failures, and weather changes, and establish a risk assessment model; S63, Risk Identification and Quantification: Based on the risk assessment model, calculate the current risk value using real-time and historical data, and then quantify and assess the risk value to determine the risk level; S64, Warning Notification Generation: Set warning rules according to risk level. When the risk value exceeds the preset threshold, trigger the warning and generate a warning notification. S65, Implementation of Preventive Measures: Based on the risk type and level, recommend appropriate preventive measures, including strengthening monitoring, adjusting construction plans, and repairing equipment. Relevant personnel shall implement the preventive measures and record the implementation status and feedback results.

8. A collaborative management system for archives of subway construction projects, used to implement the collaborative management method for archives of subway construction projects as described in any one of claims 1-7, characterized in that, Includes the following modules: Data acquisition module: Collects real-time data from the subway construction site, including project progress, material usage, and safety monitoring data, and uploads the collected real-time data to the cloud storage platform; Data classification and labeling module: Utilizes natural language processing and image recognition technologies to automatically classify uploaded real-time data and label it according to a preset labeling system; Multi-party collaborative editing and version management module: On the cloud storage platform, a virtual workspace is created, and project team members can edit, modify and supplement data in real time according to their permissions, and update it synchronously in real time. A version management mechanism is established to record the updates of real-time data and generate version numbers. Dynamic construction modeling and simulation module: Based on real-time data edited collaboratively by multiple parties, a dynamic 3D model of the construction site is established, and the construction process is simulated. By comparing real-time data with model data, the construction progress and quality are monitored in real time, potential problems are identified, and the construction plan is adjusted in a timely manner. Data security and access control module: Employs data encryption technology and a multi-level access control system, and assigns different access permissions based on user roles; Automated risk assessment and early warning module: Based on real-time and historical data, it automatically conducts risk assessments, identifies construction risks, and generates early warning notifications to prompt relevant personnel to take preventive measures; Archive and Sharing Module: After the project is completed, all construction data is archived to generate project archives, and the data is shared with relevant departments and units.

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